Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell

What is ML ?

ML  is a subdomain of AI that has demonstrated significant progress over the last decade, and remains a hot research topic. It is a branch of knowledge concerned with building algorithms that can learn from data and improve themselves with regards to the tasks they perform. ML allows computers to deduce the algorithm for some task or to extract hidden patterns from data. ML is known by several different names in different research communities: predictive analytics, data mining, statistical learning, pattern recognition, and so on. One can argue that these terms have some subtle differences, but essentially, they all overlap to the extent that you can use the terminology interchangeably.


Abbreviation ML may refer to many things outside of the AI domain; for example, there is a functional programming language of this name. Nevertheless, the abbreviation is widely used in the names of libraries and conferences as referring to ML . Throughout this book, we also use it in this way.

ML is already everywhere around us. Search engines, targeted ads, face and voice recognition, recommender systems, spam filtration, self-driven cars, fraud detection in bank systems, credit scoring, automated video captioning, and machine translation—all these things are impossible to imagine without ML these days.

Over recent years, ML has owed its success to several factors:

  • The abundance of data in different forms (big data)
  • Accessible computational power and specialized hardware (clouds and GPUs)
  • The rise of open source and open access
  • Algorithmic advances

Any ML system includes three essential components: data, model, and task. The data is something you provide as an input to your model. A model is a type of mathematical function or computer program that performs the task. For instance, your emails are data, the spam filter is a model, and telling spam apart from non-spam is a task. The learning in ML stands for a process of adjusting your model to the data so that the model becomes better at its task. The obvious consequences of this setup is expressed in the piece of wisdom well-known among statisticians,

"Your model is only as good as your data"